CourtTime : Generating Actionable Insights into Tennis Matches Using Visual Analytics

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2019
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Zusammenfassung

Tennis players and coaches of all proficiency levels seek to understand and improve their play. Summary statistics alone are inadequate to provide the insights players need to improve their games. Spatio-temporal data capturing player and ball movements is likely to provide the actionable insights needed to identify player strengths, weaknesses, and strategies. To fully utilize this spatio-temporal data, we need to integrate it with domain-relevant context meta-data. In this paper, we propose CourtTime, a novel approach to perform data-driven visual analysis of individual tennis matches. Our visual approach introduces a novel visual metaphor, namely 1-D Space-Time Charts that enable the analysis of single points at a glance based on small multiples. We also employ user-driven sorting and clustering techniques and a layout technique that aligns the last few shots in a point to facilitate shot pattern discovery. We discuss the usefulness of CourtTime via an extensive case study and report on feedback from an amateur tennis player and three tennis coaches.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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Visual analytics, tennis analysis, sports analytics, spatio-temporal analysis
Konferenz
IEEE Visual Analytics Science and Technology (VAST), IEEE Information Visualization (InfoVis), and IEEE Scientific Visualization (SciVis) 2019, 20. Okt. 2019 - 25. Okt. 2019, Vancouver, BC, Canada
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Zitieren
ISO 690POLK, Tom, Dominik JÄCKLE, Johannes HÄUSSLER, Jing YANG, 2019. CourtTime : Generating Actionable Insights into Tennis Matches Using Visual Analytics. IEEE Visual Analytics Science and Technology (VAST), IEEE Information Visualization (InfoVis), and IEEE Scientific Visualization (SciVis) 2019. Vancouver, BC, Canada, 20. Okt. 2019 - 25. Okt. 2019
BibTex
@inproceedings{Polk2019Court-46446,
  year={2019},
  title={CourtTime : Generating Actionable Insights into Tennis Matches Using Visual Analytics},
  author={Polk, Tom and Jäckle, Dominik and Häußler, Johannes and Yang, Jing}
}
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